Presentation 2

Report
Introduction to the Gene Ontology
and GO annotation resources
Rachael Huntley
UniProt-GOA
EBI
Programmatic Access to Biological Databases
3rd October 2012
What is an Ontology?
What is the difference between
an ontology and a controlled
vocabulary?
An ontology formally represents knowledge as a set
of concepts within a domain, and the relationships
among those concepts. It can be used to reason
about the entities within that domain and may be
used to describe the domain.
A Controlled vocabularies provide a way to
organize knowledge for subsequent retrieval but
does not allow reasoning about the entities.
Ontologies and CVs in Biology
Ontologies/CVs are heavily used in biological databases
• Allow organisation of data within a database
• Enable linking between databases
• Enable searches across databases
www.ebi.ac.uk/ols
What is GO?
The Gene Ontology
Less specific concepts
• A way to capture
biological knowledge for
individual gene products
in a written and
computable form
• A set of concepts
and their relationships
to each other arranged
as a hierarchy
More specific concepts
www.ebi.ac.uk/QuickGO
The Concepts in GO
•
•
1. Molecular Function
An elemental activity or task or job
protein kinase activity
insulin receptor
activity
2. Biological Process
A commonly recognised series of events
•
cell division
• mitochondrion
3. Cellular Component
Where a gene product is located
• mitochondrial matrix
• mitochondrial inner membrane
Anatomy of a GO term
Unique identifier
Term name
Synonyms
Definition
Cross-references
Ontology structure
• Directed acyclic graph
Terms can have more than one parent
• Terms are linked by
relationships
is_a
part_of
regulates (and +/- regulates)
has_part
occurs_in
www.ebi.ac.uk/QuickGO
These relationships allow for complex analysis of large datasets
Relations Between GO Terms
is_a
If A is a B, then A is a subtype of B
part_of
Wherever B exists, it is as part of A. But not all A is part of B.
A
B
http://www.geneontology.org/GO.ontology-ext.relations.shtml
All replication forks are part of a
chromosome
Not all chromosomes have
replication forks
Relations Between GO Terms
Regulates
• One process directly affects another process or quality
• Necessarily regulates: if both A and B are present, B always
regulates A, but A may not always be regulated by B
B
A
All cell cycle checkpoints regulate the cell cycle.
The cell cycle is not solely regulated by cell cycle
checkpoints
http://www.geneontology.org/GO.ontology-ext.relations.shtml
Process-Function Links in GO
• GO was originally three completely independent hierarchies, with no
relationships between them
• Biological processes are ordered assemblies of molecular functions
• As of 2009 we have started making relationships between biological
process and molecular function in the live ontology
functions that regulate processes e.g. transcription
regulator regulates transcription
process
process
function
functions that are part_of processes e.g. transporter
part_of transport
function
Searching for GO terms
Search GO terms or proteins
http://www.ebi.ac.uk/QuickGO
Exercise
Search for a GO term
Exercise 1 (pg.16)
Why do we need GO?
17
Reasons for the Gene Ontology
• Inconsistency in English language
www.geneontology.org
Inconsistency in English languauge
• Same name for different concepts
Cell
or
??
• Different names for the same concept
Eggplant
Aubergine
Brinjal
Melongene
Same for biological concepts
 Comparison is difficult – in particular across
species or across databases
Just one reason why the Gene Ontology (GO) is
is needed…
Reasons for the Gene Ontology
• Inconsistency in English language
• Increasing amounts of biological data available
• Increasing amounts of biological data to come
www.geneontology.org
Increasing amounts of biological data available
Search on ‘DNA repair’...
get over 68,000 results
Expansion of sequence
information
Reasons for the Gene Ontology
• Inconsistency in English language
• Increasing amounts of biological data available
• Increasing amounts of biological data to come
• Large datasets need to be interpreted quickly
www.geneontology.org
Aims of the GO project
• Compile the ontologies
- currently over 38,000 terms
- constantly increasing and improving
• Annotate gene products using ontology terms
- around 30 groups provide annotations
• Provide a public resource of data and tools
- regular releases of annotations
- tools for browsing/querying annotations and editing the ontology
http://www.geneontology.org
Reactome
GO Annotation
UniProt-Gene Ontology Annotation
(UniProt-GOA) project at the EBI
• Largest open-source contributor of annotations to GO
• Provides annotation for more than 350,000 species
• Our priority is to annotate the human proteome
A GO annotation is …
…a statement that a gene product;
1.
has a particular molecular function
or is involved in a particular biological process
or is located within a certain cellular component
2.
as determined by a particular method
3.
as described in a particular reference
Accession Name
P00505
GO ID
GO term name
GOT2 GO:0004069 aspartate transaminase activity
Reference
PMID:2731362
Evidence code
IDA
UniProt-GOA incorporates
annotations made using two methods
Electronic Annotation
• Quick way of producing large numbers of annotations
• Annotations use less-specific GO terms
• Only source of annotation for many non-model organism species
Manual Annotation
• Time-consuming process producing lower numbers
of annotations
• Annotations tend to use very specific GO terms
Electronic annotation methods
1. Mapping of external concepts to GO terms
GO:0005634: Nucleus
GO:0009734: Auxin mediated signaling pathway
GO:0004707: MAP kinase activity
Electronic annotation methods
2. Automatic transfer of manual annotations to orthologs
Ensembl compara
Macaque
Chimpanzee
Guinea Pig Rat
...and more
e.g. Human
Mouse
Arabidopsis
Cow
Dog
Chicken
Ensembl compara
…and more
Brachypodium
Poplar
Maize
Grape
Rice
Annotations are high-quality and have an explanation of the method (GO_REF)
http://www.geneontology.org/cgi-bin/references.cgi
Manual annotation by GOA
High–quality, specific annotations made using:
• Full text peer-reviewed papers
• A range of evidence codes to categorise
the types of evidence found in a paper
e.g. IDA, IMP, IPI
http://www.ebi.ac.uk/GOA
Number of annotations in UniProt-GOA
database
Electronic annotations
Manual annotations*
102,205,043
1,149,802
Sep 2012 Statistics
* Includes manual annotations integrated from external model organism
and specialist groups
How to access and use
GO annotation data
Where can you find annotations?
UniProtKB
Ensembl
Entrez gene
UniProt vs. QuickGO annotation display
UniProt
QuickGO
UniProt vs. QuickGO annotation display
Filtering mechanism
• Exclude root terms (e.g. Molecular Function)
• Exclude annotations with qualifier (e.g. NOT, contributes_to)
• Exclude annotations to less granular terms
• Exclude annotations to GO:0005515 protein binding
• Exclude lower quality assignments for same data, e.g. UniProt taken
in preference to MGI
• Add electronic annotations that cover ground not covered by manual
annotation
Gene Association Files
17 column files containing all information for each annotation
GO Consortium website
UniProt-GOA website
Numerous species-specific files
http://www.ebi.ac.uk/GOA/downloads.html
GO browsers
The EBI's QuickGO browser
Search GO terms
or proteins
Find sets of
GO annotations
http://www.ebi.ac.uk/QuickGO
Exercise
Find annotations to a protein
Exercise 2 (pg.16)
Find annotations to a list of proteins
Exercise 1 and 2 (pg.22)
How scientists use the GO
• Access gene product functional information
• Analyse high-throughput genomic or proteomic datasets
• Validation of experimental techniques
• Get a broad overview of a proteome
• Obtain functional information for novel gene products
Some examples…
Term enrichment
• Most popular type of GO analysis
• Determines which GO terms are more often associated
with a specified list of genes/proteins compared with a
control list or rest of genome
• Many tools available to do this analysis
• User must decide which is best for their analysis
Analysis of high-throughput genomic datasets
time
Defense response
Immune response
Response to stimulus
Toll regulated genes
JAK-STAT regulated genes
Puparial adhesion
Molting cycle
Hemocyanin
MicroArray data analysis
Amino acid catabolism
Lipid metobolism
Peptidase activity
Protein catabolism
Immune response
Immune response
Toll regulated genes
attacked control
pear
son lw n3d
... lw n3d ... Color
ed by
cted Gene
Tree:
pearson
Colored
by::
Set_LW_n3d_5p_...
List:
ch color
classification:
Set_LW_n3d_5p_...Gene
Gene
List:
Copy
ofofCopy
of(Defa...
C5_RMA (Defa...
Copy
of Copy
C5_RMA
g enes
allall
g enes
(14010) (14010)
Bregje Wertheim at the Centre for Evolutionary Genomics,
Department of Biology, UCL and Eugene Schuster Group, EBI.
Annotating novel sequences
• Can use BLAST queries to find similar sequences with
GO annotation which can be transferred to the new sequence
• Two tools currently available;
AmiGO BLAST – searches the GO Consortium database
BLAST2GO – searches the NCBI database
Using the GO to provide a functional
overview for a large dataset
• Many GO analysis tools use GO slims to give a broad
overview of the dataset
• GO slims are cut-down versions of the GO and
contain a subset of the terms in the whole GO
• GO slims usually contain less-specialised GO terms
Slimming the GO using the ‘true path rule’
Many gene products are associated with a
large number of descriptive, leaf GO nodes:
Slimming the GO using the ‘true path rule’
…however annotations can be mapped up
to a smaller set of parent GO terms:
GO slims
Custom slims are available for download;
http://www.geneontology.org/GO.slims.shtml
or you can make your own using;
• QuickGO
http://www.ebi.ac.uk/QuickGO
• AmiGO's GO slimmer
http://amigo.geneontology.org/cgi-bin/amigo/slimmer
The EBI's QuickGO browser
Search GO terms
or proteins
Find sets of
GO annotations
Map-up annotations
with GO slims
www.ebi.ac.uk/QuickGO
Precautions when using GO
annotations for analysis
• The Gene Ontology is always changing and GO annotations are
continually being created
- always use a current version of both
- if publishing your analyses please report the versions/dates you used
http://www.geneontology.org/GO.cite.shtml
• Recommended that ‘NOT’ annotations are removed before analysis
- only ~3000 out of 57 million annotations are ‘NOT’
- can confuse the analysis
Precautions when using GO
annotations for analysis
• Unannotated is not unknown
- where there is no evidence in the literature for a process, function or
location the gene product is annotated to the appropriate ontology’s
root node with an ‘ND’ evidence code (no biological data), thereby
distinguishing between unannotated and unknown
• Pay attention to under-represented GO terms
- a strong under-representation of a pathway may mean that normal
functioning of that pathway is necessary for the given condition
Exercise
Use the QuickGO web services to retrieve annotations
Exercise 1
Pg.46
The UniProt-GOA group
Project leader:
Rachael Huntley
Curators:
Yasmin Alam-Faruque
Prudence Mutowo
Software developer:
Tony Sawford
Team leaders:
Rolf Apweiler
Claire O’Donovan
Email: [email protected]
http://www.ebi.ac.uk/GOA
Acknowledgements
Members of;
UniProtKB
Ensembl
InterPro
Ensembl Genomes
IntAct
GO Consortium
HAMAP
Funding
National Human Genome Research Institute (NHGRI)
British Heart Foundation
EMBL

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